{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0b41c625",
   "metadata": {},
   "source": [
    "# Probabilistic Forecasting: Quantile Regression\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e920ab6b",
   "metadata": {},
   "source": [
    "Quantile regression is a technique for estimating the conditional quantiles of a response variable. By combining the predictions of two quantile estimators, an interval can be constructed, where each model estimates one of the interval’s boundaries. For example, models trained for $Q = 0.1$ and $Q = 0.9$ produce an 80% prediction interval ($90\\% - 10\\% = 80\\%$).\n",
    "\n",
    "If a machine learning algorithm capable of modeling quantiles is used as the `estimator` in a forecaster, the `predict` method will return predictions for a specified quantile. By creating two forecasters, each configured with a different quantile, their predictions can be combined to generate a prediction interval."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a56bb079",
   "metadata": {},
   "source": [
    "As opposed to linear regression, which is intended to estimate the conditional mean of the response variable given certain values of the predictor variables, quantile regression aims at estimating the conditional quantiles of the response variable. For a continuous distribution function, the $\\alpha$-quantile $Q_{\\alpha}(x)$ is defined such that the probability of $Y$ being smaller than $Q_{\\alpha}(x)$ is, for a given $X=x$, equal to $\\alpha$. For example, 36% of the population values are lower than the quantile  $Q=0.36$. The most known quantile is the 50%-quantile, more commonly called the median.\n",
    "\n",
    "By combining the predictions of two quantile estimators, it is possible to build an interval. Each model estimates one of the limits of the interval. For example, the models obtained for $Q = 0.1$ and $Q = 0.9$ produce an 80% prediction interval (90% - 10% = 80%).\n",
    "\n",
    "Several machine learning algorithms are capable of modeling quantiles. Some of them are:\n",
    "\n",
    "+ [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html)\n",
    "\n",
    "+ [XGBoost](https://xgboost.readthedocs.io/en/stable/python/index.html)\n",
    "\n",
    "+ [CatBoost](https://catboost.ai/en/docs/concepts/python-reference_catboostregressor)\n",
    "\n",
    "+ [Scikit-learn HistGradientBoostingRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html)\n",
    "\n",
    "+ [Scikit-learn QuantileRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.QuantileRegressor.html#sklearn.linear_model.QuantileRegressor)\n",
    "\n",
    "+ [skranger quantile RandomForest](https://skranger.readthedocs.io/en/stable/index.html)\n",
    "\n",
    "\n",
    "Just as the squared-error loss function is used to train models that predict the mean value, a specific loss function is needed in order to train models that predict quantiles. The most common metric used for quantile regression is calles [quantile loss  or pinball loss](https://en.wikipedia.org/wiki/Quantile_regression):\n",
    "\n",
    "\n",
    "$$\\text{pinball}(y, \\hat{y}) = \\frac{1}{n_{\\text{samples}}} \\sum_{i=0}^{n_{\\text{samples}}-1}  \\alpha \\max(y_i - \\hat{y}_i, 0) + (1 - \\alpha) \\max(\\hat{y}_i - y_i, 0)$$\n",
    "\n",
    "where $\\alpha$ is the target quantile, $y$ the real value and $\\hat{y}$ the quantile prediction.\n",
    "\n",
    "It can be seen that loss differs depending on the evaluated quantile. The higher the quantile, the more the loss function penalizes underestimates, and the less it penalizes overestimates. As with MSE and MAE, the goal is to minimize its values (the lower loss, the better).\n",
    "\n",
    "Two disadvantages of quantile regression, compared to the bootstrap approach to prediction intervals, are that each quantile needs its estimator and quantile regression is not available for all types of regression models. However, once the models are trained, the inference is much faster since no iterative process is needed.\n",
    "\n",
    "This type of prediction intervals can be easily estimated using [ForecasterDirect](../api/forecasterdirect.html) and [ForecasterDirectMultiVariate](../api/forecasterdirectmultivariate.html) models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85256945",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "<p>For more examples on how to use probabilistic forecasting, check out the following articles:</p>\n",
    "<ul>\n",
    "    <li>\n",
    "        <a href=\"https://cienciadedatos.net/documentos/py42-probabilistic-forecasting\" target=\"_blank\">\n",
    "            Probabilistic forecasting with machine learning\n",
    "        </a>\n",
    "    </li>\n",
    "    <li>\n",
    "        <a href=\"https://cienciadedatos.net/documentos/py60-probabilistic-forecasting-prediction-intervals-multi-step-forecasting\" target=\"_blank\">\n",
    "            Probabilistic forecasting: prediction intervals for multi-step time series forecasting\n",
    "        </a>\n",
    "    </li>\n",
    "    <li>\n",
    "        <a href=\"../faq/probabilistic-forecasting-crps-score.html\" target=\"_blank\">\n",
    "            Continuous Ranked Probability Score (CRPS) in probabilistic forecasting\n",
    "        </a>\n",
    "    </li>\n",
    "</ul>\n",
    "\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42ed835c",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(255,145,0,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #ff9100; border-color: #ff9100; padding-left: 10px; padding-right: 10px\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#ff9100; border-color: #ff1744;\"></i>\n",
    "    <b style=\"color: #ff9100;\"> <span style=\"color: #ff9100;\">&#9888;</span> Warning</b>\n",
    "</p>\n",
    "\n",
    "Forecasters of type <code>ForecasterDirect</code> are slower than <code>ForecasterRecursive</code> because they require training one model per step. Although they can achieve better performance, their scalability is an important limitation when many steps need to be predicted.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22434a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data processing\n",
    "# ==============================================================================\n",
    "import pandas as pd\n",
    "from skforecast.datasets import fetch_dataset\n",
    "\n",
    "# Plots\n",
    "# ==============================================================================\n",
    "import matplotlib.pyplot as plt\n",
    "from skforecast.plot import set_dark_theme\n",
    "\n",
    "# Modelling and Forecasting\n",
    "# ==============================================================================\n",
    "from lightgbm import LGBMRegressor\n",
    "from skforecast.direct import ForecasterDirect\n",
    "from skforecast.model_selection import TimeSeriesFold\n",
    "from skforecast.model_selection import backtesting_forecaster\n",
    "from skforecast.model_selection import bayesian_search_forecaster\n",
    "from skforecast.metrics import calculate_coverage\n",
    "from skforecast.metrics import create_mean_pinball_loss\n",
    "\n",
    "# Configuration\n",
    "# ==============================================================================\n",
    "import warnings\n",
    "warnings.filterwarnings('once')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "08237d75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────────────── <span style=\"font-weight: bold\">ett_m2_extended</span> ─────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Data from an electricity transformer station was collected between July 2016 and │\n",
       "│ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data    │\n",
       "│ points). Each data point consists of 8 features, including the date of the       │\n",
       "│ point, the predictive value \"Oil Temperature (OT)\", and 6 different types of     │\n",
       "│ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │\n",
       "│ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL),   │\n",
       "│ Low UseLess Load (LULL). Additional variables are created based on calendar      │\n",
       "│ information (year, month, week, day of the week, and hour). These variables have │\n",
       "│ been encoded using the cyclical encoding technique (sin and cos transformations) │\n",
       "│ to preserve the cyclical nature of the data.                                     │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Zhou, Haoyi &amp; Zhang, Shanghang &amp; Peng, Jieqi &amp; Zhang, Shuai &amp; Li, Jianxin &amp;      │\n",
       "│ Xiong, Hui &amp; Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for   │\n",
       "│ Long Sequence Time-Series Forecasting.                                           │\n",
       "│ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436).                   │\n",
       "│ https://github.com/zhouhaoyi/ETDataset                                           │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/ETTm2_extended.csv                                            │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 69680 rows x 16 columns                                                   │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────────────── \u001b[1mett_m2_extended\u001b[0m ─────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Data from an electricity transformer station was collected between July 2016 and │\n",
       "│ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data    │\n",
       "│ points). Each data point consists of 8 features, including the date of the       │\n",
       "│ point, the predictive value \"Oil Temperature (OT)\", and 6 different types of     │\n",
       "│ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │\n",
       "│ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL),   │\n",
       "│ Low UseLess Load (LULL). Additional variables are created based on calendar      │\n",
       "│ information (year, month, week, day of the week, and hour). These variables have │\n",
       "│ been encoded using the cyclical encoding technique (sin and cos transformations) │\n",
       "│ to preserve the cyclical nature of the data.                                     │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Zhou, Haoyi & Zhang, Shanghang & Peng, Jieqi & Zhang, Shuai & Li, Jianxin &      │\n",
       "│ Xiong, Hui & Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for   │\n",
       "│ Long Sequence Time-Series Forecasting.                                           │\n",
       "│ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436).                   │\n",
       "│ https://github.com/zhouhaoyi/ETDataset                                           │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/ETTm2_extended.csv                                            │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mShape:\u001b[0m 69680 rows x 16 columns                                                   │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OT</th>\n",
       "      <th>day_of_week_cos</th>\n",
       "      <th>day_of_week_sin</th>\n",
       "      <th>hour_cos</th>\n",
       "      <th>hour_sin</th>\n",
       "      <th>month_cos</th>\n",
       "      <th>month_sin</th>\n",
       "      <th>week_cos</th>\n",
       "      <th>week_sin</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-01 00:00:00</th>\n",
       "      <td>38.661999</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>-0.433884</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01 00:15:00</th>\n",
       "      <td>38.223000</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>-0.433884</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            OT  day_of_week_cos  day_of_week_sin  hour_cos  \\\n",
       "date                                                                         \n",
       "2016-07-01 00:00:00  38.661999        -0.900969        -0.433884       1.0   \n",
       "2016-07-01 00:15:00  38.223000        -0.900969        -0.433884       1.0   \n",
       "\n",
       "                     hour_sin  month_cos  month_sin  week_cos      week_sin  \\\n",
       "date                                                                          \n",
       "2016-07-01 00:00:00       0.0  -0.866025       -0.5      -1.0  1.224647e-16   \n",
       "2016-07-01 00:15:00       0.0  -0.866025       -0.5      -1.0  1.224647e-16   \n",
       "\n",
       "                     year  \n",
       "date                       \n",
       "2016-07-01 00:00:00  2016  \n",
       "2016-07-01 00:15:00  2016  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data\n",
    "# ==============================================================================\n",
    "data = fetch_dataset(name=\"ett_m2_extended\")\n",
    "data = data[[\n",
    "    \"OT\",\n",
    "    \"day_of_week_cos\",\n",
    "    \"day_of_week_sin\",\n",
    "    \"hour_cos\",\n",
    "    \"hour_sin\",\n",
    "    \"month_cos\",\n",
    "    \"month_sin\",\n",
    "    \"week_cos\",\n",
    "    \"week_sin\",\n",
    "    \"year\",\n",
    "]]\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "39025a9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dates train      : 2016-07-01 00:00:00 --- 2017-10-01 23:45:00  (n=43968)\n",
      "Dates validation : 2017-10-02 00:00:00 --- 2018-04-03 23:45:00  (n=17664)\n",
      "Dates test       : 2018-04-04 00:00:00 --- 2018-06-26 19:45:00  (n=8048)\n"
     ]
    }
   ],
   "source": [
    "# Split train-validation-test\n",
    "# ==============================================================================\n",
    "exog_features = data.columns.difference(['OT']).tolist()\n",
    "end_train = '2017-10-01 23:59:00'\n",
    "end_validation = '2018-04-03 23:59:00'\n",
    "data_train = data.loc[: end_train, :]\n",
    "data_val   = data.loc[end_train:end_validation, :]\n",
    "data_test  = data.loc[end_validation:, :]\n",
    "\n",
    "print(f\"Dates train      : {data_train.index.min()} --- {data_train.index.max()}  (n={len(data_train)})\")\n",
    "print(f\"Dates validation : {data_val.index.min()} --- {data_val.index.max()}  (n={len(data_val)})\")\n",
    "print(f\"Dates test       : {data_test.index.min()} --- {data_test.index.max()}  (n={len(data_test)})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2085e78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot partitions\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "plt.rcParams['lines.linewidth'] = 0.5\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "ax.plot(data_train['OT'], label='Train')\n",
    "ax.plot(data_val['OT'], label='Validation')\n",
    "ax.plot(data_test['OT'], label='Test')\n",
    "ax.set_title('Oil Temperature')\n",
    "ax.legend();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b6f918f3",
   "metadata": {},
   "source": [
    "Two quantile forecasters are trained, one for the 10% quantile and another for the 90% quantile. The predictions of both forecasters can be combined to generate a prediction interval of 80% coverage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f76c53ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create forecasters: one for each bound of the interval\n",
    "# ==============================================================================\n",
    "# The forecasters obtained for alpha=0.1 and alpha=0.9 produce a 80% confidence\n",
    "# interval (90% - 10% = 80%).\n",
    "\n",
    "# Forecaster for quantile 10%\n",
    "forecaster_q10 = ForecasterDirect(\n",
    "                     estimator = LGBMRegressor(\n",
    "                                     objective = 'quantile',\n",
    "                                     metric    = 'quantile',\n",
    "                                     alpha     = 0.1,\n",
    "                                     verbose   = -1\n",
    "                                 ),\n",
    "                     steps = 24,\n",
    "                     lags  = [1, 2, 3, 23, 24, 25, 47, 48, 49, 71, 72, 73]\n",
    "                 )\n",
    "                  \n",
    "# Forecaster for quantile 90%\n",
    "forecaster_q90 = ForecasterDirect(\n",
    "                     estimator = LGBMRegressor(\n",
    "                                     objective = 'quantile',\n",
    "                                     metric    = 'quantile',\n",
    "                                     alpha     = 0.9,\n",
    "                                     verbose   = -1\n",
    "                                 ),\n",
    "                     steps = 24,\n",
    "                     lags  = [1, 2, 3, 23, 24, 25, 47, 48, 49, 71, 72, 73]\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab380376",
   "metadata": {},
   "source": [
    "Next, a bayesian search is performed to find the best hyperparameters for the quantile estimators. When validating a quantile regression model, it is important to use a metric that is coherent with the quantile being evaluated. In this case, the pinball loss is used. Skforecast provides the function `create_mean_pinball_loss` calculate the pinball loss for a given quantile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa0ef91f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0576c23e84074e189946cb3c115bbdb6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`Forecaster` refitted using the best-found lags and parameters, and the whole data set: \n",
      "  Lags: [ 1  2  3 23 24 25 47 48 49 71 72 73] \n",
      "  Parameters: {'n_estimators': 250, 'max_depth': 3, 'learning_rate': 0.04040638127771271}\n",
      "  Backtesting metric: 0.41020051875078006\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5738d330bd5546a7ab92dfb2adc02aaf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`Forecaster` refitted using the best-found lags and parameters, and the whole data set: \n",
      "  Lags: [ 1  2  3 23 24 25 47 48 49 71 72 73] \n",
      "  Parameters: {'n_estimators': 450, 'max_depth': 8, 'learning_rate': 0.061491327557080706}\n",
      "  Backtesting metric: 0.350142766138876\n"
     ]
    }
   ],
   "source": [
    "# Bayesian search of hyper-parameters and lags for each quantile forecaster\n",
    "# ==============================================================================\n",
    "def search_space(trial):\n",
    "    search_space  = {\n",
    "        'n_estimators': trial.suggest_int('n_estimators', 100, 500, step=50),\n",
    "        'max_depth': trial.suggest_int('max_depth', 3, 10, step=1),\n",
    "        'learning_rate': trial.suggest_float('learning_rate', 0.001, 0.1)\n",
    "    }\n",
    "\n",
    "    return search_space\n",
    "\n",
    "cv = TimeSeriesFold(\n",
    "        steps              = 24,\n",
    "        initial_train_size = len(data[:end_train]),\n",
    "        refit              = False,\n",
    "    )\n",
    "\n",
    "results_grid_q10 = bayesian_search_forecaster(\n",
    "                       forecaster   = forecaster_q10,\n",
    "                       y            = data.loc[:end_validation, 'OT'],\n",
    "                       cv           = cv,\n",
    "                       metric       = create_mean_pinball_loss(alpha=0.1),\n",
    "                       search_space = search_space,\n",
    "                       n_trials     = 10,\n",
    "                       return_best  = True\n",
    "                   )\n",
    "\n",
    "results_grid_q90 = bayesian_search_forecaster(\n",
    "                       forecaster   = forecaster_q90,\n",
    "                       y            = data.loc[:end_validation, 'OT'],\n",
    "                       cv           = cv,\n",
    "                       metric       = create_mean_pinball_loss(alpha=0.9),\n",
    "                       search_space = search_space,\n",
    "                       n_trials     = 10,\n",
    "                       return_best  = True\n",
    "                   )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "02fc2645",
   "metadata": {},
   "source": [
    "### Predictions (backtesting)\n",
    "\n",
    "Once the quantile forecasters are trained, they can be used to predict each of the bounds of the forecasting interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c21063df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e5810792ebc471fb8af15c1dbb3cb1f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/336 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4871fb298da146599e757e52bf192656",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/336 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lower_bound</th>\n",
       "      <th>upper_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-04-04 00:00:00</th>\n",
       "      <td>31.848286</td>\n",
       "      <td>32.268329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04 00:15:00</th>\n",
       "      <td>31.563152</td>\n",
       "      <td>32.150039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04 00:30:00</th>\n",
       "      <td>31.269266</td>\n",
       "      <td>32.271917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04 00:45:00</th>\n",
       "      <td>31.081806</td>\n",
       "      <td>32.139591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04 01:00:00</th>\n",
       "      <td>30.670913</td>\n",
       "      <td>32.049578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     lower_bound  upper_bound\n",
       "2018-04-04 00:00:00    31.848286    32.268329\n",
       "2018-04-04 00:15:00    31.563152    32.150039\n",
       "2018-04-04 00:30:00    31.269266    32.271917\n",
       "2018-04-04 00:45:00    31.081806    32.139591\n",
       "2018-04-04 01:00:00    30.670913    32.049578"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtesting on test data\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(\n",
    "         steps              = 24, \n",
    "         initial_train_size = len(data.loc[:end_validation]),\n",
    "         refit              = False\n",
    "     )\n",
    "\n",
    "metric_q10, predictions_q10 = backtesting_forecaster(\n",
    "                                  forecaster = forecaster_q10,\n",
    "                                  y          = data['OT'],\n",
    "                                  cv         = cv,\n",
    "                                  metric     = create_mean_pinball_loss(alpha=0.1)\n",
    "                              )\n",
    "\n",
    "metric_q90, predictions_q90 = backtesting_forecaster(\n",
    "                                  forecaster = forecaster_q90,\n",
    "                                  y          = data['OT'],\n",
    "                                  cv         = cv,\n",
    "                                  metric     = create_mean_pinball_loss(alpha=0.9)\n",
    "                              )\n",
    "\n",
    "pred_intervals = pd.concat([predictions_q10, predictions_q90], axis=1)\n",
    "pred_intervals = pred_intervals.drop(columns=['fold'])\n",
    "pred_intervals.columns = ['lower_bound', 'upper_bound']\n",
    "pred_intervals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e1e43a47",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot interval\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data.loc[end_validation:, 'OT'].plot(ax=ax, label='Real value', color='orange')\n",
    "ax.fill_between(\n",
    "    data.loc[end_validation:].index,\n",
    "    pred_intervals['lower_bound'],\n",
    "    pred_intervals['upper_bound'],\n",
    "    color = 'gray',\n",
    "    alpha = 0.6,\n",
    "    zorder = 1,\n",
    "    label = '80% prediction interval'\n",
    ")\n",
    "ax.set_xlabel('')\n",
    "ax.set_title(\"Predicted intervals\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e02c0205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot intervals with zoom ['2018-05-01', '2018-05-15']\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data.loc[end_validation:, 'OT'].plot(ax=ax, label='Real value', color='orange')\n",
    "ax.fill_between(\n",
    "    data.loc[end_validation:].index,\n",
    "    pred_intervals['lower_bound'],\n",
    "    pred_intervals['upper_bound'],\n",
    "    color = 'gray',\n",
    "    alpha = 0.6,\n",
    "    zorder = 1,\n",
    "    label = '80% prediction interval'\n",
    ")\n",
    "ax.set_xlabel('')\n",
    "ax.set_title(\"Predicted intervals (zoom in)\")\n",
    "ax.set_xlim(['2018-05-01', '2018-05-15']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9ca18c6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted interval coverage: 77.37 %\n",
      "Area of the interval: 38980.18\n"
     ]
    }
   ],
   "source": [
    "# Predicted interval coverage (on test data)\n",
    "# ==============================================================================\n",
    "coverage = calculate_coverage(\n",
    "               y_true      = data.loc[end_validation:, 'OT'],\n",
    "               lower_bound = predictions_q10[\"pred\"], \n",
    "               upper_bound = predictions_q90[\"pred\"]\n",
    "           )\n",
    "print(f\"Predicted interval coverage: {round(100 * coverage, 2)} %\")\n",
    "\n",
    "# Area of the interval\n",
    "# ==============================================================================\n",
    "area = (predictions_q90[\"pred\"] - predictions_q10[\"pred\"]).sum()\n",
    "print(f\"Area of the interval: {round(area, 2)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "12d5b6bc",
   "metadata": {},
   "source": [
    "The prediction intervals generated by quantile regression achieve an empirical coverage close to the nominal coverage of 80%."
   ]
  }
 ],
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